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Build a layered context pipeline (persistent, time-sensitive, transient) that stops your agent from hallucinating #
You’ve spent weeks meticulously refining your system prompts. You’ve tried chain-of-thought reasoning, a few-shot examples, role-playing prefixes, and more. Yet, your Bedrock agent still confidently gives the wrong drug dosage, fabricates policy terms that don’t exist, or forgets information retrieved three turns ago.
The problem isn’t your prompts, but your contextual architecture — or more precisely, its lack thereof.
Why Prompt Tuning Stopped Working
In the early stages of LLM applications, prompt engineering was everything. You wrote the right instructions, and the model gave the right answers. This worked well for single-turn chatbots that read a single document.
But agents are different. As described by practitioners at Sourcegraph, an agent “runs in a loop, using different tools, gathering more states, and trying to make a decision at step 47, while residual information from steps 1 through 46 remains in its context window.” By the time your agent has delved into the sequence of tool calls…